Acute PainMARS-EDNCT05497830
Machine Learning for Risk Stratification in the Emergency Department (MARS-ED)
A study testing RISK-INDEX in people with Acute Pain and Emergencies, run by Maastricht University Medical Center at 1 site in Netherlands.
Trial summary
Rationale Identifying emergency department (ED) patients at high and low risk shortly after admission could help decision-making regarding patient care. Several clinical risk scores and triage systems for stratification of patients have been developed, but often underperform in clinical practice. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Acute Pain, Emergencies
- What is being tested?
- RISK-INDEX
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,300 participants
- Where
- 1 siteNetherlands
- Study dates
- 12 Sep 2022to 1 Nov 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Adult, defined as ≥ 18 years of age
- Assessed and treated by an internal medicine specialist (gastroenterologists included) in the ED
- Willing to give written consent, either directly or after deferred consent procedure (see section 11.2).
Who cannot join
- <4 different laboratory results available (hematology or clinical chemistry) within the first two hours of the ED visit (calculation ML prediction score otherwise not possible)
- Unwilling to provide written consent, either directly or after deferred consent procedure (see section 11.2).
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
Maastricht University Medical Centre
Maastricht, Limburg, 6229 HX
About this study
Official title: Machine Learning for Risk Stratification in the Emergency Department: A Pilot Clinical Trial
Rationale
Identifying emergency department (ED) patients at high and low risk shortly after admission could help decision-making regarding patient care. Several clinical risk scores and triage systems for stratification of patients have been developed, but often underperform in clinical practice. Moreover, most of these risk scores only have been diagnostically validated in an observational cohort, but never have been evaluated for their actual clinical impact. In a recent retrospective study that was conducted in the Maastricht University Medical Center (MUMC+), a novel clinical risk score, the RISKINDEX, was introduced that predicted 31-day mortality of sepsis patients presenting to an ED. The RISKINDEX hereby also outperformed internal medicine specialists. Observational follow-up studies underlined the potential of the risk score. However, it remains unknown to what extent these models have any beneficial value when it is actually implemented in clinical practice.
Read more
Objective
To determine the diagnostic accuracy, policy changes and clinical impact of the RISKINDEX as basis to conduct a large scale, multi-center randomised trial.
Study design
The MARS-ED study is designed as a multi-center, randomized, open-label, non-inferiority pilot clinical trial.
Study population
Adult patients who are assessed and treated by an internal medicine specialist in the ED of whom a minimum of 4 different laboratory results (hematology or clinical chemistry, required for calculation of ML risk score) are available within the first two hours of the ED visit.
Intervention
Physicians will be presented with the ML risk score (the RISKINDEX) of the patients they are actively treating, directly after assessment of regular diagnostics has taken place.
Main study parameters
Primary
- Diagnostic accuracy, policy changes and clinical impact of a novel clinical risk score (the RISKINDEX)
Secondary
- Policy changes due to presentation of ML score (treatment policy, requesting ancillary investigations, treatment restrictions (i.e., no intubation or resuscitation)
- Intensive care (ICU) and medium care (MC) admission
- Length of admission
- Mortality within 31 days
- Readmission
- Patient preference
- Feasibility of novel clinical risk score
Read the detailed description
See our protocol paper, PMID 38263188
- Conditions
- Acute Pain, Emergencies
- Being tested
- RISK-INDEX
- Keywords
- machine learning, risk stratification, pilot
- Medical subject headings
- Acute Pain, Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
RISK-INDEXOther
Presentation of RISKINDEX to the physician after approximately 2 hours. The ML RISKINDEX is a prediction model based on laboratory data from the ED. It is based on date of birth, sex and at least four laboratory data which are sampled within the first two hours of the ED visit. Laboratory data that are used as input include samples that are commonly drawn in patients that require treatment from an internal medicine physician, such as urea, albumin, C-reactive protein (CRP), lactate and bilirubin.
| Study group | Role | What they receive |
|---|---|---|
| Standard care | No intervention | Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. |
| RISKINDEX | Experimental | Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. In the intervention group, physicians will be presented with the RISKINDEX. Subsequently, self-report will again be initiated to evaluate the physicians' response to the ML score and possible policy changes due to the intervention. Receives: Other: RISK-INDEX |
What is being measured
The primary outcome is the main question the study is designed to answer.
RISK-INDEX performance
Discriminatory performance of ML risk score to predict 31-day mortality. This will be calculated using an area under the receiver operating characteristic curves (AUC).
Measured: 31 days
Policy changes
Policy changes after presentation of RISK-INDEX. This will be assessed by a filled out questionnaire by the physician where they state whether a policy change has been made as a result of the RISK-INDEX outcome.
Measured: As soon as RISK-INDEX score is presented
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Aug 2022
- Study start12 Sep 2022
- Primary completion1 Nov 2024
- Study completion1 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Acute PainStudies of the same condition, open and under way.
- More trials run by Maastricht University Medical CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,300 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Steven Meex, PhD, Principal investigator, Maastricht University Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05497830
- Sponsor's protocol ID
- NL78478.068.21
- Other IDs
- METC 21-068
- Acronym
- MARS-ED
- Status last verified
- Nov 2024
- First submitted
- 19 Jul 2022
- Last updated
- 26 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
van Dam PMEL, van Doorn WPTM, van Gils F, Sevenich L, Lambriks L, Meex SJR, Cals JWL, Stassen PM. Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department. Scand J Trauma Resusc Emerg Med. 2024 Jan 23;32(1):5. doi: 10.1186/s13049-024-01177-2.
PubMed 38263188van Dam PMEL, van Doorn WPTM, Sevenich L, Lambriks L, Cals JWL, Bekers O, Stassen PM, Meex SJR. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial. Nat Commun. 2025 Dec 1;17(1):242. doi: 10.1038/s41467-025-66947-7.
PubMed 41326390
Study at a glance
From the registry record, last updated by the sponsor on 26 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning for Risk Stratification in the Emergency Department: A Pilot Clinical Trial
- ClinicalTrials.gov ID
- NCT05497830
- Acronym / sponsor ID
- MARS-ED / NL78478.068.21
- Conditions
- Acute Pain, Emergencies
- Keywords
- machine learning, risk stratification, pilot
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,300 participants (actual)
- Lead sponsor
- Maastricht University Medical CenterUniversity, hospital or other
- Study start
- 12 Sep 2022 (actual)
- Primary completion
- 1 Nov 2024 (actual)
- Study completion
- 1 Nov 2024 (actual)
- First posted
- 11 Aug 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Rationale
Identifying emergency department (ED) patients at high and low risk shortly after admission could help decision-making regarding patient care. Several clinical risk scores and triage systems for stratification of patients have been developed, but often underperform in clinical practice. Moreover, most of these risk scores only have been diagnostically validated in an observational cohort, but never have been evaluated for their actual clinical impact. In a recent retrospective study that was conducted in the Maastricht University Medical Center (MUMC+), a novel clinical risk score, the RISKINDEX, was introduced that predicted 31-day mortality of sepsis patients presenting to an ED. The RISKINDEX hereby also outperformed internal medicine specialists. Observational follow-up studies underlined the potential of the risk score. However, it remains unknown to what extent these models have any beneficial value when it is actually implemented in clinical practice.
Objective
To determine the diagnostic accuracy, policy changes and clinical impact of the RISKINDEX as basis to conduct a large scale, multi-center randomised trial.
Study design
The MARS-ED study is designed as a multi-center, randomized, open-label, non-inferiority pilot clinical trial.
Study population
Adult patients who are assessed and treated by an internal medicine specialist in the ED of whom a minimum of 4 different laboratory results (hematology or clinical chemistry, required for calculation of ML risk score) are available within the first two hours of the ED visit.
Intervention
Physicians will be presented with the ML risk score (the RISKINDEX) of the patients they are actively treating, directly after assessment of regular diagnostics has taken place.
Main study parameters
Primary
- Diagnostic accuracy, policy changes and clinical impact of a novel clinical risk score (the RISKINDEX)
Secondary
- Policy changes due to presentation of ML score (treatment policy, requesting ancillary investigations, treatment restrictions (i.e., no intubation or resuscitation)
- Intensive care (ICU) and medium care (MC) admission
- Length of admission
- Mortality within 31 days
- Readmission
- Patient preference
- Feasibility of novel clinical risk score
Detailed description
See our protocol paper, PMID 38263188
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Standard care | No intervention | –Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. |
| RISKINDEX | Experimental | Other: RISK-INDEXRoutine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. In the intervention group, physicians will be presented with the RISKINDEX. Subsequently, self-report will again be initiated to evaluate the physicians' response to the ML score and possible policy changes due to the intervention. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Adult, defined as ≥ 18 years of age
- Assessed and treated by an internal medicine specialist (gastroenterologists included) in the ED
- Willing to give written consent, either directly or after deferred consent procedure (see section 11.2).
Key exclusion criteria (2 listed)
- <4 different laboratory results available (hematology or clinical chemistry) within the first two hours of the ED visit (calculation ML prediction score otherwise not possible)
- Unwilling to provide written consent, either directly or after deferred consent procedure (see section 11.2).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| RISK-INDEXPresentation of RISKINDEX to the physician after approximately 2 hours. The ML RISKINDEX is a prediction model based on laboratory data from the ED. It is based on date of birth, sex and at least four laboratory data which are sampled within the first two hours of the ED visit. Laboratory data that are used as input include samples that are commonly drawn in patients that require treatment from an internal medicine physician, such as urea, albumin, C-reactive protein (CRP), lactate and bilirubin. | Other | RISKINDEX |
Primary outcome
RISK-INDEX performance
Time frame: 31 days
Discriminatory performance of ML risk score to predict 31-day mortality. This will be calculated using an area under the receiver operating characteristic curves (AUC).
Policy changes
Time frame: As soon as RISK-INDEX score is presented
Policy changes after presentation of RISK-INDEX. This will be assessed by a filled out questionnaire by the physician where they state whether a policy change has been made as a result of the RISK-INDEX outcome.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Nov 2024.
- Results publication: PubMed 38263188van Dam PMEL, van Doorn WPTM, van Gils F, Sevenich L, Lambriks L, Meex SJR, Cals JWL, Stassen PM. Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department. Scand J Trauma Resusc Emerg Med. 2024 Jan 23;32(1):5. doi: 10.1186/s13049-024-01177-2.
- Derived from this study: PubMed 41326390van Dam PMEL, van Doorn WPTM, Sevenich L, Lambriks L, Cals JWL, Bekers O, Stassen PM, Meex SJR. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial. Nat Commun. 2025 Dec 1;17(1):242. doi: 10.1038/s41467-025-66947-7.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Steven Meex, PhD principal investigator
Maastricht University Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing RISK-INDEX in people with Acute Pain and Emergencies, run by Maastricht University Medical Center at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
RISK-INDEX performance (time frame: 31 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05497830, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Nov 2024; AzTrials last refreshed its copy on 1 Nov 2026.